Ammar Kamoona
AI/ML Researcher | RMIT Research Fellow | Powercor AI Scientist
- Melbourne, Australia
- RMIT University / Powercor Australia
- ResearchGate
- Github
- Google Scholar
- ORCID
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Temporal co-learning for residential electricity forecasting
less than 1 minute read
Published:
Residential electricity forecasting sits between two hard problems. Household-level load is noisy and non-stationary, while feeder-level load is smoother but loses the behavioural signal that drives the peaks. Models trained on one level of aggregation often generalise badly to another.
What growing EV adoption does to residential load profiles
1 minute read
Published:
Australian distribution networks are planning for an era in which a large share of households charge an EV at home. The interesting question is not just “how much extra energy?” but “when does that energy get drawn, and how does the shape of the residential load change?”
Detecting EV charging events online with a memory-augmented transformer
1 minute read
Published:
Distribution networks in Australia are seeing more electric vehicles every year, and network operators need to know, in near real time, when and where charging events happen. Smart-meter data offers the raw signal, but EV charging looks a lot like other high-power appliances (heat pumps, kettles, ovens) once the meter aggregates everything at the household level.
Point patterns, energy, and defect detection: an RFS view of anomalies
1 minute read
Published:
Defect detection on manufactured parts is a classic anomaly detection problem: most items are normal, defects are rare, and the visual signature of a defect varies from batch to batch. A common practical trick is to extract a set of local features from an image and score how “unusual” the set looks compared with clean examples.